Aqsa Shabbir

Apple leaf diseases represent a critical challenge for apple crop production, threatening both the quality and quantity of harvests. Leaves are essential to the growth and health of apple trees, as they play a pivotal role in photosynthesis and nutrient transport. When these leaves become infected, the diseases can rapidly spread across the entire tree, leading to significant damage that can impact the yield and the overall health of the orchard. The consequences of such outbreaks can be devastating, with severe economic losses for farmers and potentially reduced marketability of the crops. To mitigate these risks, timely and accurate detection of apple leaf diseases is crucial. Early intervention allows farmers to apply targeted treatments and prevent the spread of infection to other parts of the tree or adjacent trees in the orchard. This research proposes an improved deep learning approach, utilizing the VGG16 model, to detect early signs of apple leaf diseases. By focusing on small spots and widespread lesions, the model can identify symptoms at an early stage, allowing for prompt intervention to prevent further spread. The study employs a dataset from Kaggle and a custom repository called Apple Fungal Diseases Dataset, with the classification results compared to other leading techniques to ensure accuracy. By providing a reliable method for early detection, this research aims to support farmers in managing apple leaf diseases more effectively, ultimately contributing to healthier orchards and improved crop yields.

Hemodialysis is one treatment option for end stage renal disease which does not cure the disease completely but increases the patients’ span of life. It imposes a lot of physical and psychological burden on the patients. Identification of factors protecting the patients from these burdens is needed. Recent studies have focused on posttraumatic growth (PTG) as a protective factor against the distressing health outcomes. The aim of this cross-sectional study is to investigate the occurrence of PTG in patients on maintenance hemodialysis and examine its association with perceived social support and resilience. A convenience sample of 200 patients receiving maintenance hemodialysis was recruited from two government hospitals of Lahore, Pakistan. Data was collected using Posttraumatic Growth Inventory, Perceived Social Support Scale, and Trait Resilience Scale. Individuals undergoing hemodialysis showed high levels of PTG and the mean total score for PTGI was 60.1+ 11.5. Resilience (r = .79, p<.001) and perceived social support (r = .51, p<.001) were significantly correlated with PTG. Hierarchical multiple regression indicated increasing age, longer duration of hemodialysis, and resilience to be significant predictors (p<.001) of PTG in patients and explained 52% of the variance. The results provide helpful information which could be used to develop interventions for patients to transform their adversity into positive growth by improving their resilience and social support.

As environmental crises escalate, the need for solutions becomes ever more pressing. In current situation, media outlets play a crucial role in educating the public about environmental issues. This study investigates the role of TV channels in projecting eco-friendly content and examines how university students perceive such content. Utilizing a survey method, data was collected from 110 university students, comprising both male and female, through purposive sampling. Survey questions focused on the role of TV news channels in highlighting environmental issues. Analysis, employing t-tests and descriptive statistics, revealed that a majority of respondents believe TV channels do not allocate sufficient airtime to environmental coverage. Notably, PTV emerged as the leading channel in environmental content production. Furthermore, TV channels effectively promoted initiatives such as the "CLEAN GREEN PAKISTAN" movement and the "Adopt a Park" scheme. The study underscores the potential for TV channels to significantly contribute to environmental awareness among the masses. This research not only sheds light on media influence but also aligns with the Sustainable Development Goals, particularly those related to environmental sustainability and education.

The objective of this study was to investigate the interplay between perceived stress, sleep quality, and coping mechanisms among medical students, with a view to understand their implications for Sustainable Development Goal 3 (SDG 3) - "Good Health and Well-being," and considering environmental factors. Conducted as a cross-sectional study and 300 medical university students (152 men, 148 women), aged 18 to 25 years (mean age = 22.73, SD = 1.78), were selected using stratified random sampling based on their academic year. The study employed the Perceived Stress Scale, General Sleep Scale, and Brief COPE to assess the study variables. The results revealed gender differences in coping strategies, with female students exhibiting higher scores in avoidant and emotion-focused coping, while male students leaned towards problem-focused coping. Notably, perceived stress and avoidant coping emerged as significant predictors of poor sleep quality (p<.05) among the participants. This study found significant relationship among use of green spaces, stress, sleep quality, and coping strategies among medical students, aligning with SDG 3's objective of ensuring healthy lives and promoting well-being for all. Furthermore, while the study did not directly address environmental factors, it indirectly emphasizes the importance of creating conducive environments for health and well-being. Environmental considerations such as access to green spaces and a healthy physical environment can influence individuals' stress levels and sleep quality, contributing to holistic health outcomes. Given these findings, the study recommends that counseling centers in universities develop strategies for managing students' stress and sleep quality, while promoting healthy coping skills. By addressing these factors comprehensively, universities can contribute to enhancing students' psychological well-being and advancing progress towards SDG 3 and create sustainable and healthy environments, as outlined in SDG 13 (Climate Action), to complement initiatives focused on fostering sustainable environments conducive to health and well-being.

This study examines the health-related quality of life of people living with HIV/AIDS (PLHIVA) in Lahore, Pakistan. Participants included men, women and transgender PLHIVA, with a total sample size of N=100. An equal number of participants were divided into both groups: PLHIVA receiving antiretroviral therapy (ART) as the experimental group and those not receiving treatment, i.e. pre-ART, as the control group. Qusai's ex-post facto research design, using a non-probability purposive sampling strategy, was used in this study. Data were collected using the WHOQOL-HIV BREF scale and analyzed using correlation, t-tests, and linear regression analysis. Findings revealed higher health-related quality of life among PLHIVA on ART compared to those pre-ART. Furthermore, the primary modes of HIV/AIDS transmission were sexual contact (38%), blood transfusion (31%), and injecting drug use (33%). The implications of these findings are substantial and warrant thorough discussion.

This study investigates the influence of electronic media, particularly television, on noise pollution awareness among university students in Lahore, Pakistan. The research aims to assess the role of role of TV electronic media in raising awareness about noise pollution among university students in Lahore. The approach in this research is quantitative by using survey method through purposive sampling. This is done by collecting data from 150 students from three prominent universities, namely Lahore College for Women University, the University of Central Punjab, and Punjab University. The findings reveal that 76% students recognize noise pollution as a significant environmental threat with detrimental effects on health and the environment. Additionally, 72% respondents were affected by Traffic noise. Furthermore, there was a perceived lack of emphasis on this issue in electronic media, specifically television broadcasts. This research contributes to SDG 11 by emphasizing the importance of raising awareness about environmental issues such as noise pollution for the creation of sustainable and livable cities and communities.

Poverty is something many people are at risk of experiencing at some point in their lives. Governments across the world have been formulating and implementing poverty reduction policies to mitigate the adverse effects of poverty on their populations. The study explores the Role of TV News Channels in highlighting Poverty Reduction Policy: Ehsaas Program and analyzes that which TV News Channel is playing role effectively during COVID-19 pandemic in highlighting aspects of Ehsaas Program as perceived by general public. Online survey method was used in this study. A purposive sampling method was adopted in which a particular sample of population consisting of each gender was selected on the basis of education to evaluate general public’s opinions of Ehsaas Programs and TV News Channels. Data was collected using self-created questionnaire. Data was analyzed using SPSS. From 126 respondents in totality the results show that majority of youth of Lahore know about poverty issues and poverty reduction programs and they fully support TV News Channels in highlighting Poverty Reduction Program: Ehsaas Program under COVID-19 pandemic. The results revealed that majority of people think that TV News Channels are playing significant role in highlighting Ehsaas Program under COVID-19 situation. This research will be helpful in effectively highlighting the poverty issues and poverty reduction policies, leading to increased awareness among general public about initiatives taken by the government to deal with the issues of poor sector of public. This research will also help policy makers in planning future policies regarding poverty reduction in Pakistan.

The proliferate use of microblogs and social networks have become valuable sources to determine individuals’ opinion about an entity, product, topic, events, and politics etc. Main challenges implicated by the literature review include; contextual understanding and domain adaptation. Another challenge is that models trained on one domain may not generalize well to other domains due to differences sentiment expressions, or domain-specific terms. Therefore, textual sentiment analysis has become hotspot for research purpose. The paper proposes deep learning neural network models: LSTM and Bidirectional LSTM aims to automatically predict the sentiment polarity from given user posted text review into positive or negative class. Further, feature vectors are formed for each input sentence using Global vector for word representation (Glove) algorithm. Our proposed model utilizes 300-dimensional Glove for feature embedding. This high dimensional pre train vector contains semantically closer words. The impact of varying nature of datasets on the performance of both models for sentiment analysis is also investigated. Experiment is conducted on two Amazon product reviews datasets. The proposed research concluded that BLSTM achieved higher accuracy than Single LSTM and also outperforms the state -of-the-art models on document level reviews. Both models excel in capturing the temporal dependencies and linguistic structures. Highest accuracies of 94% and 96% are achieved on Amazon food reviews and Mobile reviews respectively using the BLSTM based model. The proposed model proved robust to changing sentiment trends or evolving language use as the datasets used are real-time datasets of thousands of users from different countries having variation in sentiment expression. The proposed model grasps the contextual understanding and domain adaptation very well.

This work deals with different design configurations using existing photon recycling technologies such as front spectral filters and back surface reflectors (BSRs) to improve the efficiency of the thermophotovoltaic (TPV) cells. On the TPV cell surface, some photons absorb, but some quantity of them is lost due to the interference on the surface. On the other hand, BSR mounted on the backside of the cell reflects all photons which were not absorbed by TPV back to the front side of the cell and the emitter, which leads to the elevated temperature of the cell and more interference on the cell surface. This work aimed to design a configuration of the TPV system model using hybrid photon recycling technologies and to investigate the efficiencies of different TPV cells with numerous factors such as emitter temperature and reflectivity of the spectral filter. The design parameters and configuration of front filters with BSRs are studied under 2500 K temperature of the emitter. It is found that an InGaAs cell with reasonable bandgap energy of 0.72 eV, is the most favorable cell material as its bandgap wavelength (1.68 μm) is closely matched to the peak wavelength (1.65 μm) of the emissions spectra. The results show that the incorporation of magnesium oxide (MgO) spectral filter along with the BSR (R = 1) and the emitter temperature of 2200 K efficiency as high as 35% can be attained. This makes MgO a viable choice in TPV cell system under concentrated solar power plant.